code: def mnist_data():
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      x_train = x_train.reshape(60000, 784)
      x_test = x_test.reshape(10000, 784)
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255
      x_test /= 255
  
      # Convert class vectors to binary class matrices
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
      return x_train, y_train, x_test, y_test
  
file_path: elephas/tests/conftest.py
imports: import pytest
  from tensorflow.keras import Sequential, Input, Model
  from tensorflow.keras.datasets import mnist, boston_housing
  from tensorflow.keras.layers import Dense, Activation, Dropout
  from tensorflow.keras.utils import to_categorical
label_desc: Use image node_id 4 for calling a global function with example usage: mnist_data() and returns: x_train,  y_train,  x_test,  y_test